clophfit.fitting.plotting
=========================

.. py:module:: clophfit.fitting.plotting

.. autoapi-nested-parse::

   Provide utilities for creating various types of plots used in this project.

   Primary functions encompassed are:

       plot_spectra: Develops a plot for spectral data. Each line is colored based
       on a designated colormap.
       plot_autovectors: Plots the autovectors.
       plot_autovalues: Plots the singular values from SVD.
       plot_fit: Plots residuals for each dataset with uncertainty.
       plot_pca: Plots the first two principal components.
       plot_spectra_distributed: Plots spectra from titration distributing on the
       figure top.
       plot_emcee: Plots emcee result.
       plot_emcee_k_on_ax: Plots emcee result for a specific parameter on an axis.
       distribute_axes: Positions axes evenly along the horizontal axis of the figure.

   The module uses several dependencies such as ArviZ, numpy, pandas, seaborn,
   lmfit, matplotlib, and uncertainties. Moreover, it includes a range of internal
   project modules and a specific color map for PCA components and LM fit.

   Helper Functions:

       _apply_common_plot_style: Applies grid style, title, and labels to a plot.
       _create_spectra_canvas: Creates figure and axes for spectra plot.

   Classes:

       PlotParameters: Parameters for plotting, depending on whether the data is pH or Cl.



Classes
-------

.. autoapisummary::

   clophfit.fitting.plotting.PlotParameters


Functions
---------

.. autoapisummary::

   clophfit.fitting.plotting.distribute_axes
   clophfit.fitting.plotting.plot_autovalues
   clophfit.fitting.plotting.plot_autovectors
   clophfit.fitting.plotting.plot_pca
   clophfit.fitting.plotting.plot_spectra
   clophfit.fitting.plotting.plot_spectra_distributed
   clophfit.fitting.plotting.plot_emcee
   clophfit.fitting.plotting.plot_emcee_k_on_ax
   clophfit.fitting.plotting.plot_fit
   clophfit.fitting.plotting.plot_fit_gemini
   clophfit.fitting.plotting.print_emcee


Module Contents
---------------

.. py:class:: PlotParameters

   Parameters for plotting, depending on whether the data is pH or Cl.


.. py:function:: distribute_axes(fig, num_axes)

   Position axes evenly along the horizontal axis of the figure.

   :param fig: The Figure object on which the Axes objects are drawn.
   :type fig: Figure
   :param num_axes: The number of Axes objects to position.
   :type num_axes: int

   :returns: A list of positioned Axes objects.
   :rtype: list[Axes]


.. py:function:: plot_autovalues(ax, s)

   Plot the singular values from SVD.

   :param ax: The mpl.axes.axes on which to plot the singular values.
   :type ax: Axes
   :param s: The singular values from the SVD.
   :type s: ArrayF


.. py:function:: plot_autovectors(ax, wl, u)

   Plot autovectors.

   :param ax: The mpl.axes.Axes object to which the plot should be added.
   :type ax: Axes
   :param wl: The index of spectra data frame.
   :type wl: pd.Index[int]
   :param u: The left singular vectors obtained from SVD.
   :type u: ArrayF


.. py:function:: plot_pca(ax, v, conc, pp)

   Plot the first two principal components.

   :param ax: The mpl.axes.Axes object to which the plot should be added.
   :type ax: Axes
   :param v: The matrix containing the principal components.
   :type v: ArrayF
   :param conc: The concentrations used for the titration.
   :type conc: ArrayF
   :param pp: The PlotParameters object containing plot parameters.
   :type pp: PlotParameters


.. py:function:: plot_spectra(ax, spectra, pp)

   Plot spectra.

   :param ax: The Mpl.Axes.Axes object to which the plot should be added.
   :type ax: Axes
   :param spectra: The DataFrame containing spectral data.
   :type spectra: pd.DataFrame
   :param pp: The PlotParameters object containing plot parameters.
   :type pp: PlotParameters


.. py:function:: plot_spectra_distributed(fig, titration, pp, dbands = None)

   Plot spectra from titration distributing on the top of the figure top.


.. py:function:: plot_emcee(flatchain)

   Plot emcee result.


.. py:function:: plot_emcee_k_on_ax(ax, res_emcee, p_name = 'K')

   Plot emcee result.


.. py:function:: plot_fit(ax, ds, params, nboot = 0, pp = None)

   Plot fitted curves and data points with uncertainty on a given Axes.

   :param ax: The matplotlib axis to plot on.
   :type ax: Axes
   :param ds: The dataset containing the data points.
   :type ds: Dataset
   :param params: The fitted parameters from lmfit.
   :type params: Parameters
   :param nboot: Number of bootstrap samples to generate confidence bands.
   :type nboot: int
   :param pp: Plotting parameters for consistent styling.
   :type pp: PlotParameters | None


.. py:function:: plot_fit_gemini(ax, ds, params, nboot = 0, pp = None)

   Plot fitted curves and data points on a given axis.

   :param ax: The matplotlib axis to plot on.
   :type ax: axes.Axes
   :param ds: The dataset containing the data points.
   :type ds: Dataset
   :param params: The fitted parameters from lmfit.
   :type params: Parameters
   :param nboot: Number of bootstrap samples to generate confidence bands.
   :type nboot: int
   :param pp: Plotting parameters for consistent styling.
   :type pp: PlotParameters | None


.. py:function:: print_emcee(result_emcee)

   Print maximum likelihood estimation (MLE) results from an emcee fitting.